\(\diamond \)Motivation
Analyzing 3D point cloud models has become an essential component of many AI systems such as additive manufacturing [1]}, autonomous driving [2]}, and augmented reality [3]}. Classifying 3D point clouds accurately in real-time can be a challenging task due to the complexity of objects that exist in a real-world environment [4]}. Among various 3D models in computer vision, point cloud representations have become increasingly popular as more data is collected by devices such as LiDAR. The goals of modeling 3D objects can be divided into three categories: part segmentation, semantic segmentation, and shape classification. Literature shows promising results utilizing sophisticated PointNet-based deep learning techniques for understanding object classifications from point clouds [4]}, [6]}. These methods, however, often require a considerable number of model parameters and are computationally expensive. In this study, we present a framework for simplifying the classification process by executing one epoch while achieving competitive results.
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